Description of the problem

Two conditions, multiple samples per condition, multiple cells per sample

Description of the problem

What cell types are affected and how?

Questions to existing data:

• Did some cell types change their expression in a similar way?
• Which genes changed their expression in a similar way?
• All other patterns in expression changes we can think of

Existing solutions

*Integrating single-cell transcriptomic data across different conditions, technologies, and species, Butler A, et al. Nature Biotechnology, 2018

Existing solutions

Align samples

scVI, Conos, ..., Seurat

See the review from the Theis lab

*Integrating single-cell transcriptomic data across different conditions, technologies, and species, Butler A, et al. Nature Biotechnology, 2018

Existing solutions

Align samples

Perform joint annotation

*Integrating single-cell transcriptomic data across different conditions, technologies, and species, Butler A, et al. Nature Biotechnology, 2018

Existing solutions

Align samples

Perform joint annotation

Run differential expression 

*Single-cell genomics identifies cell type–specific molecular changes in autism, Velmeshev D, et al. Science, 2019

Existing solutions

Align samples

Perform joint annotation

Run differential expression 

Run Gene Ontology analysis

*Single-cell genomics identifies cell type–specific molecular changes in autism, Velmeshev D, et al. Science, 2019

Existing solutions

Align samples

Perform joint annotation

Run differential expression 

Run Gene Ontology analysis

Compare cell type proportions

*A single-cell atlas of entorhinal cortex from individuals with Alzheimer’s disease reveals cell-type-specific gene expression regulation, Grubman A, Nature Neuroscience, 2019

Existing solutions

Align samples

Perform joint annotation

Run differential expression 

Run Gene Ontology analysis

Compare cell type proportions

• A single-cell atlas of entorhinal cortex from individuals with Alzheimer’s disease reveals cell-type-specific gene expression regulation, Grubman A, Nature Neuroscience (2019).

• Single-cell genomics identifies cell type–specific molecular changes in autism, Velmeshev D, et al. Science (2019).

• Mathys, H., Davila-Velderrain, J., Peng, Z. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).

• Schirmer, L., Velmeshev, D., Holmqvist, S. et al. Neuronal vulnerability and multilineage diversity in multiple sclerosis. Nature 573, 75–82 (2019).

• Wheeler, M.A., Clark, I.C., Tjon, E.C. et al. MAFG-driven astrocytes promote CNS inflammation. Nature 578, 593–599 (2020).

Why it is not enough

• What cell types are the most affected?
• How exactly are they affected?

The main questions:

Why it is not enough

Number of DE genes is not an answer

Why it is not enough

There are up to 1000 significant DE genes per type and 2795 unique DE genes in total

*Pfisterer, U., Petukhov, V., Demharter, S. et al. Identification of epilepsy-associated neuronal subtypes and gene expression underlying epileptogenesis. Nat Commun 11, 5038 (2020).

Why it is not enough

There are up to 1000 significant DE genes per type and 2795 unique DE genes in total

There are up to 300 significant GO terms per type and 796 unique terms in total

*Pfisterer, U., Petukhov, V., Demharter, S. et al. Identification of epilepsy-associated neuronal subtypes and gene expression underlying epileptogenesis. Nat Commun 11, 5038 (2020).

Why it is not enough

DE depends on the depth and quality of the annotation

Case-control analysis of single-cell studies: a fresh approach

What can we possibly do?

What can we possibly do?

Compositional analysis

Cluster-based

Cluster-free

Case-control analysis of single-cell studies: a fresh approach

Composition analysis, cluster-based

Composition analysis, cluster-based

Problem: changes are not independent

Composition analysis, cluster-based

*Schirmer, L., Velmeshev, D., Holmqvist, S. et al. Neuronal vulnerability and multilineage diversity in multiple sclerosis. Nature 573, 75–82 (2019)

Composition analysis, cluster-free

*Schirmer, L., Velmeshev, D., Holmqvist, S. et al. Neuronal vulnerability and multilineage diversity in multiple sclerosis. Nature 573, 75–82 (2019)

Composition analysis, cluster-free

*Schirmer, L., Velmeshev, D., Holmqvist, S. et al. Neuronal vulnerability and multilineage diversity in multiple sclerosis. Nature 573, 75–82 (2019)

Wilcoxon test between samples

Expression analysis, cluster-based

What cell types are affected the most?

*Pfisterer, U., Petukhov, V., Demharter, S. et al. Identification of epilepsy-associated neuronal subtypes and gene expression underlying epileptogenesis. Nat Commun 11, 5038 (2020).

Expression analysis, cluster-based

What cell types are affected the most?

Expression analysis, cluster-based

What cell types are affected the most?

Expression analysis, cluster-based

What cell types are affected the most?

Expression analysis, cluster-based

What cell types are affected the most?

Expression analysis, cluster-based

Can we trust differential expression?

Expression analysis, cluster-based

Can we trust differential expression?

Expression analysis, cluster-based

Can we trust differential expression?

*Simonsohn, Uri and Nelson, Leif D. and Simmons, Joseph P., P-Curve: A Key to the File Drawer (April 24, 2013). Journal of Experimental Psychology: General, Forthcoming

Expression analysis, cluster-free

Differential expression on single cells

Expression analysis, cluster-free

Differential expression on single cells

Expression analysis, cluster-free

Differential expression on single cells

Expression analysis, cluster-free

Differential expression on single cells

Expression analysis, cluster-free

Differential expression on single cells

Expression analysis, cluster-free

Differential expression on single cells

Expression analysis, cluster-free

Gene programs on single cells

Program 1

Hochreiter S, et. al (2010). “FABIA: Factor Analysis for Bicluster Acquisition.” Bioinformatics, 26(12), 1520-1527.

Expression analysis, cluster-free

Gene programs on single cells

Program 2

Hochreiter S, et. al (2010). “FABIA: Factor Analysis for Bicluster Acquisition.” Bioinformatics, 26(12), 1520-1527.

Expression analysis, cluster-free

Expression distances